Auditory inspired Acoustic Model for Hybrid ASR System using Gammatone based Gabor Filters

• Main Authors: Dutta, Anirban, Ashishkumar, G, Rama Rao, Ch V
• Format: Conference Proceeding
• Language: English
• Subjects: deep neural network; Feature extraction; Frequency modulation; gabor filters; gammatone filters; Hidden Markov models; Mel frequency cepstral coefficient; spectro-temporal features; Time-frequency analysis; Training
• ISSN: 2641-5542
• DOI: 10.1109/ISSPIT47144.2019.9001859

Spectro-temporal features have recently shown much performance improvement for robust Automatic Speech Recognition (ASR) tasks. Gabor filters are best known to extract the spectro-temporal cues of speech. Spectro-temporal representation becomes an essential ingredient for two dimensional Gabor based feature extraction methods. State of the art spectro-temporal features is mostly based on Mel spectrogram. However, the time-frequency representation based on the Mel scale is not accurate enough to model the human auditory system. This paper concentrates on obtaining the spectro-temporal representation by incorporating a physiologically and psychoacoustically motivated gammatone filter called gammatonegram. From literature, gammatonegram is found to better approximate the auditory perception of speech. The spectro-temporal features obtained using gammatonegram based Gabor filters are fed to a hybrid Deep Neural Network (DNN)-Hidden Markov Model (HMM) framework to develop the acoustic model of an ASR system. Experimental analysis is carried out with NOISEX-92 database implemented on TIMIT. The experimental results show the better performance gain obtained with the proposed features compared with conventional feature extraction methods.